Molecule discovery is a pivotal research field, impacting everything from medicine to materials. Recently, Large Language Models (LLMs) have been widely adopted in molecular understanding and generation, serving as a bridge between the molecular space and the natural language space, yet the alignment between molecules and their corresponding captions remains a significant challenge. Previous endea
Mixed precision quantization has become an important technique for optimizing the execution of deep neural networks (DNNs). Certified robustness, which provides provable guarantees about a model's ability to withstand different adversarial perturbations, has rarely been addressed in quantization due to the unacceptably high cost of certifying robustness. This paper introduces ARQ, an innovative mi
FlashAttention improves efficiency through tiling, but its online softmax still relies on floating-point arithmetic for numerical stability, making full quantization difficult. We identify three main obstacles to integer-only FlashAttention: (1) scale explosion during tile-wise accumulation, (2) inefficient shift-based exponential operations on GPUs, and (3) quantization granularity constraints re
Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining machine learning performance due to the time-intensive nature of manual annotations. This work introduces a novel approach that leverages outpainting to mitigate annotated data scarcity by generating artificial contexts and annotations, significantly reducing labeling efforts. We apply th
Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are private, scarce and structurally heterogeneous, making internet-scale pre-training infeasible. To overcome this data scarcity, We introduce $\textbf{RDB-PFN}$, the first relational foundation model trained purely via $\
Capital is concentrated in a few outsized, hard-to-classify entries rather than a broad round count. That makes the desk more about where balance-sheet power sits than about early-stage momentum.
Capital is concentrated in a few outsized, hard-to-classify entries rather than a broad round count. That makes the desk more about where balance-sheet power sits than about early-stage momentum.
Capital is concentrated in a few outsized, hard-to-classify entries rather than a broad round count. That makes the desk more about where balance-sheet power sits than about early-stage momentum.
Named-person signal is limited, but the moves that do appear point to institutional reshuffling rather than routine churn. The desk matters when a role change alters regulatory, industrial, or frontier-lab leverage.
Named-person signal is limited, but the moves that do appear point to institutional reshuffling rather than routine churn. The desk matters when a role change alters regulatory, industrial, or frontier-lab leverage.
Named-person signal is limited, but the moves that do appear point to institutional reshuffling rather than routine churn. The desk matters when a role change alters regulatory, industrial, or frontier-lab leverage.
Benchmark movement is concentrated in vision and reasoning surfaces, with fresh scores that matter for ranking pressure. The desk shows where model capability is being measured most visibly, not just claimed.
Benchmark movement is concentrated in vision and reasoning surfaces, with fresh scores that matter for ranking pressure. The desk shows where model capability is being measured most visibly, not just claimed.
Benchmark movement is concentrated in vision and reasoning surfaces, with fresh scores that matter for ranking pressure. The desk shows where model capability is being measured most visibly, not just claimed.